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Purpose: We present an updated study evaluating the performance of large language models (LLMs) in answering radiation oncology physics questions, focusing on the recently released models.
Methods: A set of 100 multiple choice radiation oncology physics questions, previously created by a well-experienced physicist, was used for this study. The answer options of the questions were randomly shuffled to create "new" exam sets.
Application of large language models in clinical record correction: a comprehensive study on various retraining methods.
J Am Med Inform Assoc
December 2024
CEIEC, Universidad Francisco de Vitoria, Pozuelo de Alarcón, 28223 Madrid, Spain.
Objectives: We evaluate the effectiveness of large language models (LLMs), specifically GPT-based (GPT-3.5 and GPT-4) and Llama-2 models (13B and 7B architectures), in autonomously assessing clinical records (CRs) to enhance medical education and diagnostic skills.
Materials And Methods: Various techniques, including prompt engineering, fine-tuning (FT), and low-rank adaptation (LoRA), were implemented and compared on Llama-2 7B.
Large Language Model Ability to Translate CT and MRI Free-Text Radiology Reports Into Multiple Languages.
Radiology
December 2024
From the Departments of Neuroradiology (A.M., M.P.W.) and Radiology (S.L.), Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Department of Neuroradiology, Hôpital Maison-Blanche, CHU Reims, Université Reims-Champagne-Ardenne, 45 Rue Cognacq-Jay, 51092 Reims, France (A.M.); Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany (A.M.); School of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany (F.B., L.A., M.R.M., K.B.); Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy (L.U.); Department of Radiology, Karolinska University Hospital, Stockholm, Sweden (E.K.); Department for Clinical Science, Intervention and Technology (CLINTEC), Division of Radiology, Karolinska Institute, Stockholm, Sweden (A.T.); Department of Radiology, National Institute Mongi Ben Hamida of Neurology, Tunis, Tunisia (S.J., I.D.); Department of Radiology, School of Medicine, University of Crete, Heraklion, Greece (M.E.K., M.T.); Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion, Greece (M.E.K.); Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, Turkey (B.K.); Department of Radiology, Koc University Hospital, Istanbul, Turkey (S.Y.); Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School of Nanjing University, Nanjing, China (L.Z., B.H.); Laboratory for Digital Public Health Technologies, ITMO University, St Petersburg, Russian Federation (A.A., E.A.Y., T.L.); Department of Radiology, Chiang Mai University, Chiang Mai, Thailand (W.M., S.A.); Department of Medicine, Surgery, and Dentistry, University of Salerno, Baronissi, Italy (R.C.); and School of Medicine and Health, Institute for Cardiovascular Radiology and Nuclear Medicine, German Heart Center Munich, TUM University Hospital, Technical University of Munich, Munich, Germany (K.B.).
Background High-quality translations of radiology reports are essential for optimal patient care. Because of limited availability of human translators with medical expertise, large language models (LLMs) are a promising solution, but their ability to translate radiology reports remains largely unexplored. Purpose To evaluate the accuracy and quality of various LLMs in translating radiology reports across high-resource languages (English, Italian, French, German, and Chinese) and low-resource languages (Swedish, Turkish, Russian, Greek, and Thai).
View Article and Find Full Text PDFCLIP-Llama: A New Approach for Scene Text Recognition with a Pre-Trained Vision-Language Model and a Pre-Trained Language Model.
Sensors (Basel)
November 2024
College of Computer Science and Technology, Xinjiang University, No. 777 Huarui Street, Urumqi 830017, China.
This study focuses on Scene Text Recognition (STR), which plays a crucial role in various applications of artificial intelligence such as image retrieval, office automation, and intelligent transportation systems. Currently, pre-trained vision-language models have become the foundation for various downstream tasks. CLIP exhibits robustness in recognizing both regular (horizontal) and irregular (rotated, curved, blurred, or occluded) text in natural images.
View Article and Find Full Text PDFAssessment of Large Language Models in Cataract Care Information Provision: A Quantitative Comparison.
Ophthalmol Ther
January 2025
Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China.
Introduction: Cataracts are a significant cause of blindness. While individuals frequently turn to the Internet for medical advice, distinguishing reliable information can be challenging. Large language models (LLMs) have attracted attention for generating accurate, human-like responses that may be used for medical consultation.
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